10 Bits: the Data News Hotlist

This week’s list of data news highlights covers September 16-22, 2017, and includes articles about the Washington, D.C. government using AI to make intersections safer and an autonomous drone delivery system for hospitals.

A group of 21 neuroscience laboratories in Europe and the United States have launched an initiative called the International Brain Lab (IBL) to study how the entire brain makes decisions related to foraging, a behavior that all animals exhibit. Neuroscience models typically focus on how a select group of brain circuits relate to a particular behavior, while IBL will work to develop a model of how the brain as a whole functions. For the first two years of the project, IBL will build informatics tools to automatically share data between researchers, and participating scientists are required to make their research data public.

A Cambridge, Massachusetts startup called Humatics is developing a radio-frequency tracking system capable of tracking multiple objects with millimeter-precision to help robots better detect nearby human workers in industrial settings. Traditional systems that use depth sensors or cameras can only give robots an approximate position of a nearby person in a 3D space, and can be limited by lighting conditions. Humatics’ system can give robots a much more granular understanding of how humans are working around them, enabling applications such as robots passing workers tools or running analytics on how workers move about a warehouse.

Washington, D.C. has partnered with Microsoft to launch a public video analytics platform to develop training data for an AI system that can determine what makes an intersection dangerous. Members of the public can access the platform online and highlight different objects, such as cars, bicycles, and pedestrians, to turn the footage into easy-to-understand training data. Eventually, this data could help D.C. develop automated solutions for its commitments to the Vision Zero initiative to eliminate traffic deaths.

German startup PEAT has developed a smartphone app called Plantix that uses machine learning to analyze pictures of a user’s crops and identify any signs of disease. Plantix can recognize signs of 400 different diseases or pests, such as powdery mildew or an aphid infestation. Plantix also allows users to share information about their crops with other users to help each other diagnose problems.

Logistics company Matternet has partnered with Switzerland’s national postal service Swiss Post to deploy a permanent network of autonomous drones to fly between hospitals in Switzerland to deliver medical supplies. The drones will fly over urban areas carrying blood tests, lab samples, and other supplies, while delivery stations automatically route the drones to avoid potential collisions. The system is capable of ferrying supplies between hospitals within 30 minutes.

A startup called iSee is developing autonomous driving software that uses AI to mimic human intuition to make it better at responding to unfamiliar circumstances on the road. Machine learning algorithms in self-driving cars can become adept at recognizing obstacles, road conditions, and other vehicles after large amounts of training, but are confused when they encounter new situations because they cannot generalize their knowledge. iSee is focusing its research on teaching AI systems to infer physical properties about the world without much training, and transfer what it learns from one situation to another.

The North Carolina Department of Transportation has conducted a test to use drones to scan and analyze trash sites, finding that drones could perform the task four times faster than traditional methods without sacrificing accuracy. The test used staged car crashes and had a drone scan, record, and analyze the scene to produce a digital model of the crash site in under 25 minutes accurate up to 0.03 feet.

Finnish startup GrainSense has developed a handheld scanner that can determine the nutritional content of crops including wheat, oats, rye, and barley in under five seconds. The scanner uses different frequencies of near-infrared light to determine the levels of protein, carbohydrate, fat, and moisture—a technique commonly used in agriculture labs, however they require farmers to send a large sample to a lab and wait days or weeks to see the results. The GrainSense scanner is the first device that allows farmers to perform these tests in the field, as well as log the GPS coordinates of every measurement.

A startup called Aeva has developed a prototype sensor system for self-driving cars that can capture more detailed data about a car’s surroundings than traditional LIDAR as well as track the velocity of nearby objects. LIDAR uses pulses of laser light to map a car’s surroundings, while Aeva’s system uses a continuous wave of light to capture higher-resolution data with a greater range.

An app called Natural Cycles that uses an algorithm and user-submitted body temperature data has proven to be 99 percent effective at preventing unwanted pregnancy when used correctly, which is more effective than birth control pills. Users enter daily body temperature readings, which fluctuate based on changes in progesterone levels, which indicate fertility, and Natural Cycles will determine whether or not it is safe for a user to have unprotected sex without risk of pregnancy.

Joshua New is a senior policy analyst at the Center for Data Innovation. He has a background in government affairs, policy, and communication. Prior to joining the Center for Data Innovation, Joshua graduated from American University with degrees in C.L.E.G. (Communication, Legal Institutions, Economics, and Government) and Public Communication. His research focuses on methods of promoting innovative and emerging technologies as a means of improving the economy and quality of life. Follow Joshua on Twitter @Josh_A_New.